How sample paths of leaky integrate-and-fire models are influenced by the presence of a firing threshold

摘要: Neural membrane potential data are necessarily conditional on observation being prior to a firing time. In stochastic leaky integrate-and-fire model, this corresponds conditioning the process not crossing boundary. literature, simulation and estimation have almost always been done using unconditioned processes. letter, we determine differential equations of diffusion conditioned stay below level S up fixed time t1 cross boundary for first at t1. This allows sample paths identification corresponding mean process. Differences between free processes illustrated, as well role noise in increasing these differences.